most citedTransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering

1 citations · 1 across the 1 of their papers we have counts for

collaborators

8 papers

cs.LG20261 cited

TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering

Keyang Chen, Mingxuan Jiang, Yongsheng Zhao +9

Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However, progress remains stifled b…

cs.DC2026

HARP: Orchestrating Automated Parallel Training on Heterogeneous GPU Clusters

Antian Liang, Zhigang Zhao, Kai Zhang +6

With the rapid evolution of GPU architectures, the heterogeneity of model training infrastructures is steadily increasing. In such environments, effectively utilizing all available…

cs.DB2026

UTune: Towards Uncertainty-Aware Online Index Tuning

Chenning Wu, Sifan Chen, Wentao Wu +4

There have been a flurry of recent proposals on learned benefit estimators for index tuning. Although these learned estimators show promising improvement over what-if query optimiz…

cs.DB2025

ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQL

Yiwen Jiao, Tonghui Ren, Yuche Gao +4

Large Language Models (LLMs) have demonstrated remarkable progress in translating natural language to SQL, but a significant semantic gap persists between their general knowledge a…

cs.DB2025

MLego: Interactive and Scalable Topic Exploration Through Model Reuse

Fei Ye, Jiapan Liu, Yinan Jing +3

With massive texts on social media, users and analysts often rely on topic modeling techniques to quickly extract key themes and gain insights. Traditional topic modeling technique…

cs.DB2025

Survey of Filtered Approximate Nearest Neighbor Search over the Vector-Scalar Hybrid Data

Yanjun Lin, Kai Zhang, Zhenying He +2

Filtered approximate nearest neighbor search (FANNS), an extension of approximate nearest neighbor search (ANNS) that incorporates scalar filters, has been widely applied to constr…